Overview

Dataset statistics

Number of variables23
Number of observations5399
Missing cells361
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory542.4 B

Variable types

Numeric16
Boolean7

Alerts

potential_issue has constant value "False" Constant
oe_constraint has constant value "False" Constant
national_inv is highly correlated with sales_9_monthHigh correlation
in_transit_qty is highly correlated with forecast_3_month and 7 other fieldsHigh correlation
forecast_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
forecast_6_month is highly correlated with in_transit_qty and 8 other fieldsHigh correlation
forecast_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_1_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_9_month is highly correlated with national_inv and 8 other fieldsHigh correlation
min_bank is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
perf_6_month_avg is highly correlated with perf_12_month_avgHigh correlation
perf_12_month_avg is highly correlated with perf_6_month_avgHigh correlation
pieces_past_due is highly correlated with forecast_6_month and 1 other fieldsHigh correlation
local_bo_qty is highly correlated with pieces_past_dueHigh correlation
stop_auto_buy is highly correlated with lead_timeHigh correlation
went_on_backorder is highly correlated with potential_issue and 1 other fieldsHigh correlation
deck_risk is highly correlated with lead_timeHigh correlation
ppap_risk is highly correlated with potential_issue and 1 other fieldsHigh correlation
potential_issue is highly correlated with stop_auto_buy and 5 other fieldsHigh correlation
oe_constraint is highly correlated with stop_auto_buy and 5 other fieldsHigh correlation
rev_stop is highly correlated with potential_issue and 1 other fieldsHigh correlation
lead_time is highly correlated with deck_risk and 1 other fieldsHigh correlation
lead_time has 361 (6.7%) missing values Missing
national_inv is highly skewed (γ1 = 31.33974204) Skewed
in_transit_qty is highly skewed (γ1 = 52.37164737) Skewed
forecast_6_month is highly skewed (γ1 = 22.1929959) Skewed
forecast_9_month is highly skewed (γ1 = 23.76410595) Skewed
sales_1_month is highly skewed (γ1 = 20.43889742) Skewed
sales_3_month is highly skewed (γ1 = 20.29849041) Skewed
sales_6_month is highly skewed (γ1 = 22.33594794) Skewed
sales_9_month is highly skewed (γ1 = 21.00267975) Skewed
min_bank is highly skewed (γ1 = 27.18121113) Skewed
pieces_past_due is highly skewed (γ1 = 40.59990285) Skewed
local_bo_qty is highly skewed (γ1 = 59.13402525) Skewed
sku has unique values Unique
national_inv has 375 (6.9%) zeros Zeros
in_transit_qty has 4409 (81.7%) zeros Zeros
forecast_3_month has 3861 (71.5%) zeros Zeros
forecast_6_month has 3548 (65.7%) zeros Zeros
forecast_9_month has 3382 (62.6%) zeros Zeros
sales_1_month has 3140 (58.2%) zeros Zeros
sales_3_month has 2521 (46.7%) zeros Zeros
sales_6_month has 2192 (40.6%) zeros Zeros
sales_9_month has 1987 (36.8%) zeros Zeros
min_bank has 2880 (53.3%) zeros Zeros
pieces_past_due has 5340 (98.9%) zeros Zeros
perf_6_month_avg has 128 (2.4%) zeros Zeros
perf_12_month_avg has 87 (1.6%) zeros Zeros
local_bo_qty has 5323 (98.6%) zeros Zeros

Reproduction

Analysis started2022-09-30 07:41:50.654791
Analysis finished2022-09-30 07:42:33.094227
Duration42.44 seconds
Software versionpandas-profiling v3.3.1
Download configurationconfig.json

Variables

sku
Real number (ℝ≥0)

UNIQUE

Distinct5399
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1114130.429
Minimum1026827
Maximum1237426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:33.217808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1026827
5-th percentile1111788.9
Q11112868.5
median1114218
Q31115567.5
95-th percentile1116647.1
Maximum1237426
Range210599
Interquartile range (IQR)2699

Descriptive statistics

Standard deviation8962.790253
Coefficient of variation (CV)0.008044650806
Kurtosis86.27023799
Mean1114130.429
Median Absolute Deviation (MAD)1350
Skewness3.017674098
Sum6015190186
Variance80331609.12
MonotonicityNot monotonic
2022-09-30T07:42:33.380700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10268271
 
< 0.1%
11150771
 
< 0.1%
11150851
 
< 0.1%
11150841
 
< 0.1%
11150831
 
< 0.1%
11150821
 
< 0.1%
11150811
 
< 0.1%
11150801
 
< 0.1%
11150791
 
< 0.1%
11150781
 
< 0.1%
Other values (5389)5389
99.8%
ValueCountFrequency (%)
10268271
< 0.1%
10433841
< 0.1%
10436961
< 0.1%
10438521
< 0.1%
10440481
< 0.1%
10441981
< 0.1%
10446431
< 0.1%
10450981
< 0.1%
10458151
< 0.1%
10458671
< 0.1%
ValueCountFrequency (%)
12374261
< 0.1%
12365371
< 0.1%
12351371
< 0.1%
12267261
< 0.1%
12251371
< 0.1%
12214151
< 0.1%
12208081
< 0.1%
12191651
< 0.1%
12185751
< 0.1%
12183781
< 0.1%

national_inv
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct776
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.4076681
Minimum-1186
Maximum129280
Zeros375
Zeros (%)6.9%
Negative21
Negative (%)0.4%
Memory size42.3 KiB
2022-09-30T07:42:33.550602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1186
5-th percentile0
Q14
median14
Q372
95-th percentile893.4
Maximum129280
Range130466
Interquartile range (IQR)68

Descriptive statistics

Standard deviation2487.163691
Coefficient of variation (CV)7.986841514
Kurtosis1415.158317
Mean311.4076681
Median Absolute Deviation (MAD)12
Skewness31.33974204
Sum1681290
Variance6185983.226
MonotonicityNot monotonic
2022-09-30T07:42:33.725315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0375
 
6.9%
2332
 
6.1%
3286
 
5.3%
5213
 
3.9%
4205
 
3.8%
1182
 
3.4%
10157
 
2.9%
8142
 
2.6%
6135
 
2.5%
9131
 
2.4%
Other values (766)3241
60.0%
ValueCountFrequency (%)
-11861
< 0.1%
-4991
< 0.1%
-941
< 0.1%
-581
< 0.1%
-551
< 0.1%
-541
< 0.1%
-481
< 0.1%
-351
< 0.1%
-341
< 0.1%
-291
< 0.1%
ValueCountFrequency (%)
1292801
< 0.1%
442861
< 0.1%
416731
< 0.1%
403201
< 0.1%
376571
< 0.1%
356121
< 0.1%
354961
< 0.1%
271711
< 0.1%
234521
< 0.1%
199061
< 0.1%

lead_time
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct24
Distinct (%)0.5%
Missing361
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean7.844382692
Minimum0
Maximum52
Zeros25
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:33.877183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median8
Q38
95-th percentile12
Maximum52
Range52
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.896811635
Coefficient of variation (CV)0.8792038719
Kurtosis27.38105868
Mean7.844382692
Median Absolute Deviation (MAD)1
Skewness4.632144674
Sum39520
Variance47.56601073
MonotonicityNot monotonic
2022-09-30T07:42:34.009617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
82224
41.2%
21041
19.3%
12608
 
11.3%
4415
 
7.7%
9384
 
7.1%
5290
 
1.7%
359
 
1.1%
1056
 
1.0%
1435
 
0.6%
1625
 
0.5%
Other values (14)101
 
1.9%
(Missing)361
 
6.7%
ValueCountFrequency (%)
025
 
0.5%
21041
19.3%
359
 
1.1%
4415
 
7.7%
513
 
0.2%
618
 
0.3%
71
 
< 0.1%
82224
41.2%
9384
 
7.1%
1056
 
1.0%
ValueCountFrequency (%)
5290
1.7%
401
 
< 0.1%
302
 
< 0.1%
261
 
< 0.1%
222
 
< 0.1%
211
 
< 0.1%
203
 
0.1%
1710
 
0.2%
1625
 
0.5%
158
 
0.1%

in_transit_qty
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct238
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.01741063
Minimum0
Maximum27430
Zeros4409
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:34.164990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile51
Maximum27430
Range27430
Interquartile range (IQR)0

Descriptive statistics

Standard deviation422.2830546
Coefficient of variation (CV)15.07216567
Kurtosis3302.60745
Mean28.01741063
Median Absolute Deviation (MAD)0
Skewness52.37164737
Sum151266
Variance178322.9782
MonotonicityNot monotonic
2022-09-30T07:42:34.336540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04409
81.7%
1121
 
2.2%
268
 
1.3%
357
 
1.1%
445
 
0.8%
538
 
0.7%
638
 
0.7%
1028
 
0.5%
1222
 
0.4%
821
 
0.4%
Other values (228)552
 
10.2%
ValueCountFrequency (%)
04409
81.7%
1121
 
2.2%
268
 
1.3%
357
 
1.1%
445
 
0.8%
538
 
0.7%
638
 
0.7%
721
 
0.4%
821
 
0.4%
920
 
0.4%
ValueCountFrequency (%)
274301
< 0.1%
60001
< 0.1%
51361
< 0.1%
38401
< 0.1%
35001
< 0.1%
34171
< 0.1%
30002
< 0.1%
29201
< 0.1%
27301
< 0.1%
26521
< 0.1%

forecast_3_month
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct383
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.21892943
Minimum0
Maximum20000
Zeros3861
Zeros (%)71.5%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:34.509277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile240.5
Maximum20000
Range20000
Interquartile range (IQR)2

Descriptive statistics

Standard deviation732.1334813
Coefficient of variation (CV)7.93908025
Kurtosis376.4008846
Mean92.21892943
Median Absolute Deviation (MAD)0
Skewness17.33825262
Sum497890
Variance536019.4345
MonotonicityNot monotonic
2022-09-30T07:42:34.680823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03861
71.5%
2101
 
1.9%
188
 
1.6%
566
 
1.2%
1059
 
1.1%
459
 
1.1%
350
 
0.9%
644
 
0.8%
2030
 
0.6%
829
 
0.5%
Other values (373)1012
 
18.7%
ValueCountFrequency (%)
03861
71.5%
188
 
1.6%
2101
 
1.9%
350
 
0.9%
459
 
1.1%
566
 
1.2%
644
 
0.8%
724
 
0.4%
829
 
0.5%
912
 
0.2%
ValueCountFrequency (%)
200001
< 0.1%
198001
< 0.1%
186601
< 0.1%
150001
< 0.1%
144001
< 0.1%
113281
< 0.1%
108001
< 0.1%
95321
< 0.1%
94801
< 0.1%
86921
< 0.1%

forecast_6_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct498
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.8697907
Minimum0
Maximum65484
Zeros3548
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:34.863479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile500
Maximum65484
Range65484
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1564.630997
Coefficient of variation (CV)8.372840743
Kurtosis700.8567086
Mean186.8697907
Median Absolute Deviation (MAD)0
Skewness22.1929959
Sum1008910
Variance2448070.157
MonotonicityNot monotonic
2022-09-30T07:42:35.032905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03548
65.7%
192
 
1.7%
292
 
1.7%
471
 
1.3%
560
 
1.1%
1059
 
1.1%
358
 
1.1%
849
 
0.9%
643
 
0.8%
742
 
0.8%
Other values (488)1285
 
23.8%
ValueCountFrequency (%)
03548
65.7%
192
 
1.7%
292
 
1.7%
358
 
1.1%
471
 
1.3%
560
 
1.1%
643
 
0.8%
742
 
0.8%
849
 
0.9%
913
 
0.2%
ValueCountFrequency (%)
654841
< 0.1%
334801
< 0.1%
300002
< 0.1%
270001
< 0.1%
260001
< 0.1%
212161
< 0.1%
191581
< 0.1%
176041
< 0.1%
162921
< 0.1%
156601
< 0.1%

forecast_9_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct577
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.241341
Minimum0
Maximum102252
Zeros3382
Zeros (%)62.6%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:35.205765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile740.5
Maximum102252
Range102252
Interquartile range (IQR)14

Descriptive statistics

Standard deviation2295.900244
Coefficient of variation (CV)8.311211624
Kurtosis833.115744
Mean276.241341
Median Absolute Deviation (MAD)0
Skewness23.76410595
Sum1491427
Variance5271157.932
MonotonicityNot monotonic
2022-09-30T07:42:35.371620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03382
62.6%
298
 
1.8%
192
 
1.7%
468
 
1.3%
361
 
1.1%
1058
 
1.1%
556
 
1.0%
648
 
0.9%
744
 
0.8%
841
 
0.8%
Other values (567)1451
26.9%
ValueCountFrequency (%)
03382
62.6%
192
 
1.7%
298
 
1.8%
361
 
1.1%
468
 
1.3%
556
 
1.0%
648
 
0.9%
744
 
0.8%
841
 
0.8%
925
 
0.5%
ValueCountFrequency (%)
1022521
< 0.1%
478801
< 0.1%
432001
< 0.1%
400001
< 0.1%
380001
< 0.1%
301441
< 0.1%
275641
< 0.1%
270001
< 0.1%
269641
< 0.1%
240421
< 0.1%

sales_1_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct292
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.62900537
Minimum0
Maximum9608
Zeros3140
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:35.564063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile86
Maximum9608
Range9608
Interquartile range (IQR)4

Descriptive statistics

Standard deviation239.5093818
Coefficient of variation (CV)7.572460119
Kurtosis604.3847259
Mean31.62900537
Median Absolute Deviation (MAD)0
Skewness20.43889742
Sum170765
Variance57364.74396
MonotonicityNot monotonic
2022-09-30T07:42:35.743050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03140
58.2%
1486
 
9.0%
2257
 
4.8%
3149
 
2.8%
4120
 
2.2%
582
 
1.5%
778
 
1.4%
667
 
1.2%
857
 
1.1%
1044
 
0.8%
Other values (282)919
 
17.0%
ValueCountFrequency (%)
03140
58.2%
1486
 
9.0%
2257
 
4.8%
3149
 
2.8%
4120
 
2.2%
582
 
1.5%
667
 
1.2%
778
 
1.4%
857
 
1.1%
934
 
0.6%
ValueCountFrequency (%)
96081
< 0.1%
50471
< 0.1%
45681
< 0.1%
43721
< 0.1%
40701
< 0.1%
37721
< 0.1%
32001
< 0.1%
30411
< 0.1%
27441
< 0.1%
26301
< 0.1%

sales_3_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct470
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.50879793
Minimum0
Maximum29327
Zeros2521
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:35.907889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q312
95-th percentile283.3
Maximum29327
Range29327
Interquartile range (IQR)12

Descriptive statistics

Standard deviation753.3293783
Coefficient of variation (CV)7.64733094
Kurtosis576.9960854
Mean98.50879793
Median Absolute Deviation (MAD)1
Skewness20.29849041
Sum531849
Variance567505.1522
MonotonicityNot monotonic
2022-09-30T07:42:36.073645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02521
46.7%
1427
 
7.9%
2291
 
5.4%
3169
 
3.1%
4149
 
2.8%
5109
 
2.0%
690
 
1.7%
776
 
1.4%
860
 
1.1%
1057
 
1.1%
Other values (460)1450
26.9%
ValueCountFrequency (%)
02521
46.7%
1427
 
7.9%
2291
 
5.4%
3169
 
3.1%
4149
 
2.8%
5109
 
2.0%
690
 
1.7%
776
 
1.4%
860
 
1.1%
948
 
0.9%
ValueCountFrequency (%)
293271
< 0.1%
166251
< 0.1%
151801
< 0.1%
149601
< 0.1%
129211
< 0.1%
125761
< 0.1%
124941
< 0.1%
83641
< 0.1%
82441
< 0.1%
74231
< 0.1%

sales_6_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct612
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.5065753
Minimum0
Maximum65994
Zeros2192
Zeros (%)40.6%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:36.234455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q324
95-th percentile548.4
Maximum65994
Range65994
Interquartile range (IQR)24

Descriptive statistics

Standard deviation1565.089201
Coefficient of variation (CV)7.924238467
Kurtosis717.2056926
Mean197.5065753
Median Absolute Deviation (MAD)2
Skewness22.33594794
Sum1066338
Variance2449504.208
MonotonicityNot monotonic
2022-09-30T07:42:36.396775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02192
40.6%
1395
 
7.3%
2248
 
4.6%
3162
 
3.0%
4149
 
2.8%
5115
 
2.1%
694
 
1.7%
776
 
1.4%
966
 
1.2%
864
 
1.2%
Other values (602)1838
34.0%
ValueCountFrequency (%)
02192
40.6%
1395
 
7.3%
2248
 
4.6%
3162
 
3.0%
4149
 
2.8%
5115
 
2.1%
694
 
1.7%
776
 
1.4%
864
 
1.2%
966
 
1.2%
ValueCountFrequency (%)
659941
< 0.1%
365101
< 0.1%
297851
< 0.1%
266201
< 0.1%
245911
< 0.1%
245171
< 0.1%
237561
< 0.1%
187251
< 0.1%
165711
< 0.1%
159721
< 0.1%

sales_9_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct723
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.6140026
Minimum0
Maximum98664
Zeros1987
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:36.571981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q338
95-th percentile833.2
Maximum98664
Range98664
Interquartile range (IQR)38

Descriptive statistics

Standard deviation2457.414503
Coefficient of variation (CV)7.962744666
Kurtosis621.3112777
Mean308.6140026
Median Absolute Deviation (MAD)3
Skewness21.00267975
Sum1666207
Variance6038886.04
MonotonicityNot monotonic
2022-09-30T07:42:36.741711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01987
36.8%
1381
 
7.1%
2240
 
4.4%
4155
 
2.9%
3147
 
2.7%
5114
 
2.1%
695
 
1.8%
779
 
1.5%
869
 
1.3%
958
 
1.1%
Other values (713)2074
38.4%
ValueCountFrequency (%)
01987
36.8%
1381
 
7.1%
2240
 
4.4%
3147
 
2.7%
4155
 
2.9%
5114
 
2.1%
695
 
1.8%
779
 
1.5%
869
 
1.3%
958
 
1.1%
ValueCountFrequency (%)
986641
< 0.1%
546161
< 0.1%
471361
< 0.1%
447271
< 0.1%
423411
< 0.1%
402401
< 0.1%
384321
< 0.1%
301661
< 0.1%
273801
< 0.1%
253211
< 0.1%

min_bank
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct311
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.81718837
Minimum0
Maximum12283
Zeros2880
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:36.913708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile105
Maximum12283
Range12283
Interquartile range (IQR)2

Descriptive statistics

Standard deviation263.0925763
Coefficient of variation (CV)8.016913982
Kurtosis1042.257159
Mean32.81718837
Median Absolute Deviation (MAD)0
Skewness27.18121113
Sum177180
Variance69217.7037
MonotonicityNot monotonic
2022-09-30T07:42:37.082615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02880
53.3%
1826
 
15.3%
2356
 
6.6%
3115
 
2.1%
479
 
1.5%
551
 
0.9%
643
 
0.8%
728
 
0.5%
1926
 
0.5%
1021
 
0.4%
Other values (301)974
 
18.0%
ValueCountFrequency (%)
02880
53.3%
1826
 
15.3%
2356
 
6.6%
3115
 
2.1%
479
 
1.5%
551
 
0.9%
643
 
0.8%
728
 
0.5%
815
 
0.3%
915
 
0.3%
ValueCountFrequency (%)
122831
< 0.1%
74581
< 0.1%
50061
< 0.1%
38621
< 0.1%
38081
< 0.1%
30891
< 0.1%
29501
< 0.1%
28041
< 0.1%
24551
< 0.1%
22041
< 0.1%

potential_issue
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5399 
ValueCountFrequency (%)
False5399
100.0%
2022-09-30T07:42:37.227808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

pieces_past_due
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct28
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7464345249
Minimum0
Maximum1134
Zeros5340
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:37.337737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1134
Range1134
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.74293457
Coefficient of variation (CV)27.78935577
Kurtosis1911.300861
Mean0.7464345249
Median Absolute Deviation (MAD)0
Skewness40.59990285
Sum4030
Variance430.2693347
MonotonicityNot monotonic
2022-09-30T07:42:37.479466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
05340
98.9%
112
 
0.2%
26
 
0.1%
45
 
0.1%
55
 
0.1%
35
 
0.1%
104
 
0.1%
62
 
< 0.1%
141
 
< 0.1%
11341
 
< 0.1%
Other values (18)18
 
0.3%
ValueCountFrequency (%)
05340
98.9%
112
 
0.2%
26
 
0.1%
35
 
0.1%
45
 
0.1%
55
 
0.1%
62
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
11341
< 0.1%
6001
< 0.1%
5401
< 0.1%
4301
< 0.1%
3361
< 0.1%
1501
< 0.1%
1441
< 0.1%
1331
< 0.1%
1201
< 0.1%
801
< 0.1%

perf_6_month_avg
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct98
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.861296536
Minimum-99
Maximum1
Zeros128
Zeros (%)2.4%
Negative468
Negative (%)8.7%
Memory size42.3 KiB
2022-09-30T07:42:37.642549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q10.63
median0.82
Q30.97
95-th percentile1
Maximum1
Range100
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation28.08100973
Coefficient of variation (CV)-3.572058324
Kurtosis6.637265205
Mean-7.861296536
Median Absolute Deviation (MAD)0.16
Skewness-2.938364431
Sum-42443.14
Variance788.5431074
MonotonicityNot monotonic
2022-09-30T07:42:37.816178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1510
 
9.4%
-99468
 
8.7%
0.99454
 
8.4%
0.77317
 
5.9%
0.82288
 
5.3%
0.98253
 
4.7%
0.96175
 
3.2%
0.69162
 
3.0%
0.97157
 
2.9%
0.89134
 
2.5%
Other values (88)2481
46.0%
ValueCountFrequency (%)
-99468
8.7%
0128
 
2.4%
0.024
 
0.1%
0.031
 
< 0.1%
0.041
 
< 0.1%
0.055
 
0.1%
0.066
 
0.1%
0.0710
 
0.2%
0.089
 
0.2%
0.0915
 
0.3%
ValueCountFrequency (%)
1510
9.4%
0.99454
8.4%
0.98253
4.7%
0.97157
 
2.9%
0.96175
 
3.2%
0.9599
 
1.8%
0.94101
 
1.9%
0.93122
 
2.3%
0.9272
 
1.3%
0.91104
 
1.9%

perf_12_month_avg
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.478770143
Minimum-99
Maximum1
Zeros87
Zeros (%)1.6%
Negative447
Negative (%)8.3%
Memory size42.3 KiB
2022-09-30T07:42:38.574552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q10.65
median0.81
Q30.95
95-th percentile0.99
Maximum1
Range100
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation27.50041029
Coefficient of variation (CV)-3.677130032
Kurtosis7.175074444
Mean-7.478770143
Median Absolute Deviation (MAD)0.14
Skewness-3.028464027
Sum-40377.88
Variance756.2725663
MonotonicityNot monotonic
2022-09-30T07:42:38.741801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99447
 
8.3%
0.99434
 
8.0%
0.8338
 
6.3%
0.98281
 
5.2%
0.97212
 
3.9%
0.79202
 
3.7%
0.68194
 
3.6%
0.95179
 
3.3%
0.96155
 
2.9%
1148
 
2.7%
Other values (91)2809
52.0%
ValueCountFrequency (%)
-99447
8.3%
087
 
1.6%
0.013
 
0.1%
0.028
 
0.1%
0.033
 
0.1%
0.041
 
< 0.1%
0.057
 
0.1%
0.0613
 
0.2%
0.074
 
0.1%
0.083
 
0.1%
ValueCountFrequency (%)
1148
 
2.7%
0.99434
8.0%
0.98281
5.2%
0.97212
3.9%
0.96155
 
2.9%
0.95179
3.3%
0.94119
 
2.2%
0.93104
 
1.9%
0.9295
 
1.8%
0.91119
 
2.2%

local_bo_qty
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6210409335
Minimum0
Maximum1447
Zeros5323
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2022-09-30T07:42:38.896652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1447
Range1447
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.54688766
Coefficient of variation (CV)34.69479466
Kurtosis3831.324837
Mean0.6210409335
Median Absolute Deviation (MAD)0
Skewness59.13402525
Sum3353
Variance464.2683679
MonotonicityNot monotonic
2022-09-30T07:42:39.030138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
05323
98.6%
126
 
0.5%
37
 
0.1%
26
 
0.1%
64
 
0.1%
73
 
0.1%
43
 
0.1%
602
 
< 0.1%
142
 
< 0.1%
152
 
< 0.1%
Other values (19)21
 
0.4%
ValueCountFrequency (%)
05323
98.6%
126
 
0.5%
26
 
0.1%
37
 
0.1%
43
 
0.1%
64
 
0.1%
73
 
0.1%
81
 
< 0.1%
91
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
14471
< 0.1%
5251
< 0.1%
2361
< 0.1%
1621
< 0.1%
1201
< 0.1%
1051
< 0.1%
961
< 0.1%
602
< 0.1%
562
< 0.1%
501
< 0.1%

deck_risk
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
4088 
True
1311 
ValueCountFrequency (%)
False4088
75.7%
True1311
 
24.3%
2022-09-30T07:42:39.173633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

oe_constraint
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5399 
ValueCountFrequency (%)
False5399
100.0%
2022-09-30T07:42:39.282351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

ppap_risk
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
4750 
True
649 
ValueCountFrequency (%)
False4750
88.0%
True649
 
12.0%
2022-09-30T07:42:39.388247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

stop_auto_buy
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
True
5231 
False
 
168
ValueCountFrequency (%)
True5231
96.9%
False168
 
3.1%
2022-09-30T07:42:39.502638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

rev_stop
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5397 
True
 
2
ValueCountFrequency (%)
False5397
> 99.9%
True2
 
< 0.1%
2022-09-30T07:42:39.608418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

went_on_backorder
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
False
5348 
True
 
51
ValueCountFrequency (%)
False5348
99.1%
True51
 
0.9%
2022-09-30T07:42:39.718751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Interactions

2022-09-30T07:42:29.731115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:53.187049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:55.724104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:58.044456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:00.540353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:02.810632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:05.220581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:07.826707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:10.112726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:12.430710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:14.870849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:17.574396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:19.883568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:22.250512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:24.744877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:27.417884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:29.873926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:53.656133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:55.861476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:58.191987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:00.668663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:02.954897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:05.360207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:07.956112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:10.245645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:12.564700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:15.004699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:17.715779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:20.021495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:22.389135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:24.879693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:27.550743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:30.020571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:53.796416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:56.012995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:58.332309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:00.803175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:03.108901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:05.507789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:08.097257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:10.394333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:12.706974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:15.152937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:17.856591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:20.164223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:22.540148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:25.021716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:27.702594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:30.161977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:53.936479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:56.156433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:41:58.470464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:00.939414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:03.255945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:05.652251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:08.235171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:10.545473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:12.845760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-09-30T07:42:07.105918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:09.389865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:11.722582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:14.009328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:16.832491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:19.151173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:21.509951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:24.038860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-09-30T07:42:29.129117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-09-30T07:42:26.999370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:29.276930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-09-30T07:42:19.596904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-09-30T07:42:29.429274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-09-30T07:42:02.667854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:05.060210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:07.679835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:09.960011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:12.281476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:14.715307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:17.424850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:19.735987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:22.098500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:24.600387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:27.274430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-30T07:42:29.576552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-30T07:42:39.839338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-30T07:42:40.054692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-30T07:42:40.262475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-30T07:42:40.467364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-30T07:42:40.635831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-30T07:42:32.253767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-30T07:42:32.722190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-30T07:42:32.917486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

skunational_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stopwent_on_backorder
010268270NaN000000000No0-99.00-99.000NoNoNoYesNoNo
1104338429.0000000000No00.990.990NoNoNoYesNoNo
210436962NaN000000000No0-99.00-99.000YesNoNoYesNoNo
3104385278.0000000001No00.100.130NoNoNoYesNoNo
410440488NaN000000042No0-99.00-99.000YesNoNoYesNoNo
51044198138.0000000000No00.820.870NoNoNoYesNoNo
610446431095NaN000000004No0-99.00-99.000YesNoNoYesNoNo
7104509862.0000000000No00.000.000YesNoYesYesNoNo
81045815140NaN01511415200000No0-99.00-99.000NoNoNoYesNoNo
9104586748.0000000000No00.820.870NoNoNoYesNoNo

Last rows

skunational_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stopwent_on_backorder
5389111686738.0001301220No00.840.900NoNoNoYesNoNo
53901116868-78.005696112133056760No00.970.927NoNoNoYesNoYes
53911116869518.0104610215216429516017No00.990.860NoNoNoYesNoNo
53921116870688.001753254075112040158842No00.770.820NoNoNoYesNoNo
539311168719298.032176134895091593202641346579153No00.990.980NoNoNoYesNoNo
539411168721682.020001657108184559No00.930.950NoNoNoYesNoNo
5395111687302.0014141400000No00.990.980YesNoNoYesNoNo
5396111687458.0000000000No00.950.960NoNoNoYesNoNo
53971116875189.000510137110No00.850.830NoNoNoYesNoNo
5398111687664.0026882439560No00.770.800NoNoNoYesNoNo